Generalized Circuit Topology of Qn-Hybrid-NPC Multilevel Converter With Novel Decomposed Sensor-Less Modulation Method
Bibliographic record
Abstract
In this paper, a novel generalized circuit topology of the n-time quadrupled-hybrid-neutral-point-clamped (Qn-HNPC) converter and a new decomposed sensor-less modulation method are proposed to multiply the number of output voltage levels of the 5L-HNPC converter with a less number of components. The Qn-HNPC converter is realized by applying $n$ number of the proposed voltage-level multiplier modules (VLMMs) to the 5L-HNPC converter. Hence, the number of output voltage levels is n-time quadrupled in the attained Qn-HNPC converter, whereas a less number of components, as well as, low cost and size, are added to the main 5L-HNPC converter. Moreover, a novel generalized decomposed sensor-less modulation method is introduced to decompose the main 5L-HNPC and the proposed VLMMs reference signals in the attained Qn-HNPC converter. Consequently, only four low-voltage and low-power switches operate at switching frequency, whereas the remaining devices commutate at the low or fundamental frequency. Furthermore, employing the proposed modulation method in the attained Qn-HNPC converter causes self-balancing of all the capacitors voltage, doubling the first switching harmonic cluster frequency of the output voltage, as well as a remarkable decrease in the control complexity, the stored energy, and the size of the VLMMs capacitor. The presented simulation and experimental results verify the performance and viability of the proposed Qn-HNPC converter topology, as well as, the suggested decomposed modulation method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".